Overview

Dataset statistics

Number of variables17
Number of observations143596
Missing cells462
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.6 MiB
Average record size in memory136.0 B

Variable types

Text6
Categorical4
Numeric7

Alerts

Postal Code is highly overall correlated with StateHigh correlation
Model Year is highly overall correlated with Electric Range and 1 other fieldsHigh correlation
Electric Range is highly overall correlated with Model Year and 2 other fieldsHigh correlation
Legislative District is highly overall correlated with StateHigh correlation
2020 Census Tract is highly overall correlated with StateHigh correlation
State is highly overall correlated with Postal Code and 2 other fieldsHigh correlation
Make is highly overall correlated with Electric Vehicle Type and 1 other fieldsHigh correlation
Electric Vehicle Type is highly overall correlated with Electric Range and 2 other fieldsHigh correlation
Clean Alternative Fuel Vehicle (CAFV) Eligibility is highly overall correlated with Model Year and 3 other fieldsHigh correlation
State is highly imbalanced (99.4%)Imbalance
Postal Code is highly skewed (γ1 = -31.09395073)Skewed
2020 Census Tract is highly skewed (γ1 = -26.78672314)Skewed
DOL Vehicle ID has unique valuesUnique
Electric Range has 63954 (44.5%) zerosZeros
Base MSRP has 140151 (97.6%) zerosZeros

Reproduction

Analysis started2023-10-12 06:46:23.643825
Analysis finished2023-10-12 06:48:16.974943
Duration1 minute and 53.33 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct9311
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:17.319539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1435960
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1785 ?
Unique (%)1.2%

Sample

1st row5UXTA6C03P
2nd row1FMCU0EZXN
3rd row1G1FW6S03J
4th row5YJSA1AC0D
5th row1FADP5CU8F
ValueCountFrequency (%)
7saygdee7p 652
 
0.5%
7saygdee2p 636
 
0.4%
7saygdee6p 632
 
0.4%
7saygdee8p 628
 
0.4%
7saygdee0p 619
 
0.4%
7saygdeexp 618
 
0.4%
7saygdee5p 613
 
0.4%
7saygdee4p 612
 
0.4%
7saygdee9p 612
 
0.4%
7saygdee3p 590
 
0.4%
Other values (9301) 137384
95.7%
2023-10-11T23:48:17.732902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 130108
 
9.1%
1 108447
 
7.6%
A 85684
 
6.0%
Y 83698
 
5.8%
J 79090
 
5.5%
5 73980
 
5.2%
3 60748
 
4.2%
P 59452
 
4.1%
G 54024
 
3.8%
D 53185
 
3.7%
Other values (24) 647544
45.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 977429
68.1%
Decimal Number 458531
31.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 130108
13.3%
A 85684
 
8.8%
Y 83698
 
8.6%
J 79090
 
8.1%
P 59452
 
6.1%
G 54024
 
5.5%
D 53185
 
5.4%
N 52399
 
5.4%
C 51203
 
5.2%
S 42606
 
4.4%
Other values (14) 285980
29.3%
Decimal Number
ValueCountFrequency (%)
1 108447
23.7%
5 73980
16.1%
3 60748
13.2%
4 41577
 
9.1%
7 39417
 
8.6%
0 38313
 
8.4%
6 33499
 
7.3%
2 30446
 
6.6%
8 17917
 
3.9%
9 14187
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 977429
68.1%
Common 458531
31.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 130108
13.3%
A 85684
 
8.8%
Y 83698
 
8.6%
J 79090
 
8.1%
P 59452
 
6.1%
G 54024
 
5.5%
D 53185
 
5.4%
N 52399
 
5.4%
C 51203
 
5.2%
S 42606
 
4.4%
Other values (14) 285980
29.3%
Common
ValueCountFrequency (%)
1 108447
23.7%
5 73980
16.1%
3 60748
13.2%
4 41577
 
9.1%
7 39417
 
8.6%
0 38313
 
8.4%
6 33499
 
7.3%
2 30446
 
6.6%
8 17917
 
3.9%
9 14187
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1435960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 130108
 
9.1%
1 108447
 
7.6%
A 85684
 
6.0%
Y 83698
 
5.8%
J 79090
 
5.5%
5 73980
 
5.2%
3 60748
 
4.2%
P 59452
 
4.1%
G 54024
 
3.8%
D 53185
 
3.7%
Other values (24) 647544
45.1%

County
Text

Distinct170
Distinct (%)0.1%
Missing22
Missing (%)< 0.1%
Memory size1.1 MiB
2023-10-11T23:48:17.904400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.4543929
Min length3

Characters and Unicode

Total characters783109
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)0.1%

Sample

1st rowKing
2nd rowYakima
3rd rowKing
4th rowKing
5th rowKitsap
ValueCountFrequency (%)
king 75383
51.8%
snohomish 16429
 
11.3%
pierce 11017
 
7.6%
clark 8455
 
5.8%
thurston 5097
 
3.5%
kitsap 4725
 
3.2%
spokane 3542
 
2.4%
whatcom 3529
 
2.4%
benton 1757
 
1.2%
skagit 1599
 
1.1%
Other values (176) 13867
 
9.5%
2023-10-11T23:48:18.198018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 114210
14.6%
n 112339
14.3%
K 80835
10.3%
g 77533
9.9%
o 50350
 
6.4%
h 42655
 
5.4%
a 36028
 
4.6%
s 31601
 
4.0%
e 31201
 
4.0%
r 28129
 
3.6%
Other values (42) 178228
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 635876
81.2%
Uppercase Letter 145397
 
18.6%
Space Separator 1826
 
0.2%
Other Punctuation 9
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 114210
18.0%
n 112339
17.7%
g 77533
12.2%
o 50350
7.9%
h 42655
 
6.7%
a 36028
 
5.7%
s 31601
 
5.0%
e 31201
 
4.9%
r 28129
 
4.4%
m 22186
 
3.5%
Other values (15) 89644
14.1%
Uppercase Letter
ValueCountFrequency (%)
K 80835
55.6%
S 22808
 
15.7%
P 11250
 
7.7%
C 10960
 
7.5%
T 5100
 
3.5%
W 4530
 
3.1%
B 1775
 
1.2%
J 1672
 
1.1%
I 1594
 
1.1%
G 958
 
0.7%
Other values (13) 3915
 
2.7%
Other Punctuation
ValueCountFrequency (%)
' 5
55.6%
. 4
44.4%
Space Separator
ValueCountFrequency (%)
1826
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 781273
99.8%
Common 1836
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 114210
14.6%
n 112339
14.4%
K 80835
10.3%
g 77533
9.9%
o 50350
 
6.4%
h 42655
 
5.5%
a 36028
 
4.6%
s 31601
 
4.0%
e 31201
 
4.0%
r 28129
 
3.6%
Other values (38) 176392
22.6%
Common
ValueCountFrequency (%)
1826
99.5%
' 5
 
0.3%
. 4
 
0.2%
- 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 783109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 114210
14.6%
n 112339
14.3%
K 80835
10.3%
g 77533
9.9%
o 50350
 
6.4%
h 42655
 
5.4%
a 36028
 
4.6%
s 31601
 
4.0%
e 31201
 
4.0%
r 28129
 
3.6%
Other values (42) 178228
22.8%

City
Text

Distinct655
Distinct (%)0.5%
Missing22
Missing (%)< 0.1%
Memory size1.1 MiB
2023-10-11T23:48:18.559422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length22
Mean length8.227179
Min length3

Characters and Unicode

Total characters1181209
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique201 ?
Unique (%)0.1%

Sample

1st rowSeattle
2nd rowMoxee
3rd rowSeattle
4th rowNewcastle
5th rowBremerton
ValueCountFrequency (%)
seattle 24662
 
14.8%
bellevue 7376
 
4.4%
redmond 5245
 
3.1%
vancouver 5045
 
3.0%
bothell 4599
 
2.8%
kirkland 4455
 
2.7%
island 4285
 
2.6%
sammamish 4199
 
2.5%
renton 3744
 
2.2%
olympia 3396
 
2.0%
Other values (689) 99683
59.8%
2023-10-11T23:48:18.951749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 161791
13.7%
a 115219
 
9.8%
l 105142
 
8.9%
t 82902
 
7.0%
n 77542
 
6.6%
o 69034
 
5.8%
r 49104
 
4.2%
i 47071
 
4.0%
S 41142
 
3.5%
d 39999
 
3.4%
Other values (42) 392263
33.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 991114
83.9%
Uppercase Letter 166835
 
14.1%
Space Separator 23115
 
2.0%
Dash Punctuation 145
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 161791
16.3%
a 115219
11.6%
l 105142
10.6%
t 82902
8.4%
n 77542
 
7.8%
o 69034
 
7.0%
r 49104
 
5.0%
i 47071
 
4.7%
d 39999
 
4.0%
m 38424
 
3.9%
Other values (15) 204886
20.7%
Uppercase Letter
ValueCountFrequency (%)
S 41142
24.7%
B 21416
12.8%
R 11317
 
6.8%
K 8608
 
5.2%
L 8385
 
5.0%
M 8303
 
5.0%
V 7836
 
4.7%
T 6926
 
4.2%
I 6617
 
4.0%
P 6392
 
3.8%
Other values (15) 39893
23.9%
Space Separator
ValueCountFrequency (%)
23115
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1157949
98.0%
Common 23260
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 161791
14.0%
a 115219
 
10.0%
l 105142
 
9.1%
t 82902
 
7.2%
n 77542
 
6.7%
o 69034
 
6.0%
r 49104
 
4.2%
i 47071
 
4.1%
S 41142
 
3.6%
d 39999
 
3.5%
Other values (40) 369003
31.9%
Common
ValueCountFrequency (%)
23115
99.4%
- 145
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1181209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 161791
13.7%
a 115219
 
9.8%
l 105142
 
8.9%
t 82902
 
7.0%
n 77542
 
6.6%
o 69034
 
5.8%
r 49104
 
4.2%
i 47071
 
4.0%
S 41142
 
3.5%
d 39999
 
3.4%
Other values (42) 392263
33.2%

State
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
WA
143269 
CA
 
90
VA
 
36
MD
 
31
TX
 
21
Other values (38)
 
149

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters287192
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 143269
99.8%
CA 90
 
0.1%
VA 36
 
< 0.1%
MD 31
 
< 0.1%
TX 21
 
< 0.1%
NC 14
 
< 0.1%
CO 11
 
< 0.1%
AZ 10
 
< 0.1%
IL 9
 
< 0.1%
OR 8
 
< 0.1%
Other values (33) 97
 
0.1%

Length

2023-10-11T23:48:19.054262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wa 143269
99.8%
ca 90
 
0.1%
va 36
 
< 0.1%
md 31
 
< 0.1%
tx 21
 
< 0.1%
nc 14
 
< 0.1%
co 11
 
< 0.1%
az 10
 
< 0.1%
il 9
 
< 0.1%
or 8
 
< 0.1%
Other values (33) 97
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 143423
49.9%
W 143271
49.9%
C 135
 
< 0.1%
V 41
 
< 0.1%
M 41
 
< 0.1%
D 39
 
< 0.1%
N 38
 
< 0.1%
T 31
 
< 0.1%
O 25
 
< 0.1%
L 21
 
< 0.1%
Other values (15) 127
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 287192
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 143423
49.9%
W 143271
49.9%
C 135
 
< 0.1%
V 41
 
< 0.1%
M 41
 
< 0.1%
D 39
 
< 0.1%
N 38
 
< 0.1%
T 31
 
< 0.1%
O 25
 
< 0.1%
L 21
 
< 0.1%
Other values (15) 127
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 287192
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 143423
49.9%
W 143271
49.9%
C 135
 
< 0.1%
V 41
 
< 0.1%
M 41
 
< 0.1%
D 39
 
< 0.1%
N 38
 
< 0.1%
T 31
 
< 0.1%
O 25
 
< 0.1%
L 21
 
< 0.1%
Other values (15) 127
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 143423
49.9%
W 143271
49.9%
C 135
 
< 0.1%
V 41
 
< 0.1%
M 41
 
< 0.1%
D 39
 
< 0.1%
N 38
 
< 0.1%
T 31
 
< 0.1%
O 25
 
< 0.1%
L 21
 
< 0.1%
Other values (15) 127
 
< 0.1%

Postal Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct791
Distinct (%)0.6%
Missing22
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean98175.213
Minimum1730
Maximum99403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:19.142422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile98006
Q198052
median98122
Q398370
95-th percentile98926
Maximum99403
Range97673
Interquartile range (IQR)318

Descriptive statistics

Standard deviation2383.1691
Coefficient of variation (CV)0.024274652
Kurtosis1019.0449
Mean98175.213
Median Absolute Deviation (MAD)99
Skewness-31.093951
Sum1.4095408 × 1010
Variance5679494.9
MonotonicityNot monotonic
2023-10-11T23:48:19.232659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 3689
 
2.6%
98012 2605
 
1.8%
98033 2526
 
1.8%
98004 2372
 
1.7%
98006 2359
 
1.6%
98115 2263
 
1.6%
98074 2039
 
1.4%
98072 2028
 
1.4%
98040 2011
 
1.4%
98034 1968
 
1.4%
Other values (781) 119714
83.4%
ValueCountFrequency (%)
1730 1
< 0.1%
1731 1
< 0.1%
1824 1
< 0.1%
3804 1
< 0.1%
6355 1
< 0.1%
6371 1
< 0.1%
6379 2
< 0.1%
6385 1
< 0.1%
6443 2
< 0.1%
7003 1
< 0.1%
ValueCountFrequency (%)
99403 50
 
< 0.1%
99402 9
 
< 0.1%
99362 278
0.2%
99361 10
 
< 0.1%
99360 4
 
< 0.1%
99357 15
 
< 0.1%
99356 1
 
< 0.1%
99354 248
0.2%
99353 181
 
0.1%
99352 512
0.4%

Model Year
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.8656
Minimum1997
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:19.312577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2014
Q12018
median2021
Q32022
95-th percentile2023
Maximum2024
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0160038
Coefficient of variation (CV)0.0014931706
Kurtosis0.17296475
Mean2019.8656
Median Absolute Deviation (MAD)2
Skewness-0.93844323
Sum2.9004462 × 108
Variance9.096279
MonotonicityNot monotonic
2023-10-11T23:48:19.388340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2023 30904
21.5%
2022 27904
19.4%
2021 18470
12.9%
2018 14392
10.0%
2020 11126
 
7.7%
2019 10662
 
7.4%
2017 8577
 
6.0%
2016 5662
 
3.9%
2015 4938
 
3.4%
2013 4596
 
3.2%
Other values (12) 6365
 
4.4%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 4
 
< 0.1%
2000 9
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 19
 
< 0.1%
2010 25
 
< 0.1%
2011 798
0.6%
2012 1642
1.1%
ValueCountFrequency (%)
2024 273
 
0.2%
2023 30904
21.5%
2022 27904
19.4%
2021 18470
12.9%
2020 11126
 
7.7%
2019 10662
 
7.4%
2018 14392
10.0%
2017 8577
 
6.0%
2016 5662
 
3.9%
2015 4938
 
3.4%

Make
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
TESLA
65552 
NISSAN
13317 
CHEVROLET
11816 
FORD
7307 
BMW
 
6209
Other values (32)
39395 

Length

Max length20
Median length14
Mean length5.5482813
Min length3

Characters and Unicode

Total characters796711
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBMW
2nd rowFORD
3rd rowCHEVROLET
4th rowTESLA
5th rowFORD

Common Values

ValueCountFrequency (%)
TESLA 65552
45.7%
NISSAN 13317
 
9.3%
CHEVROLET 11816
 
8.2%
FORD 7307
 
5.1%
BMW 6209
 
4.3%
KIA 5922
 
4.1%
TOYOTA 5074
 
3.5%
VOLKSWAGEN 3914
 
2.7%
VOLVO 3415
 
2.4%
JEEP 3084
 
2.1%
Other values (27) 17986
 
12.5%

Length

2023-10-11T23:48:19.470082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 65552
45.6%
nissan 13317
 
9.3%
chevrolet 11816
 
8.2%
ford 7307
 
5.1%
bmw 6209
 
4.3%
kia 5922
 
4.1%
toyota 5074
 
3.5%
volkswagen 3914
 
2.7%
volvo 3415
 
2.4%
jeep 3084
 
2.1%
Other values (32) 18050
 
12.6%

Most occurring characters

ValueCountFrequency (%)
E 107438
13.5%
A 105606
13.3%
S 103700
13.0%
T 89900
11.3%
L 88742
11.1%
O 42598
 
5.3%
N 38750
 
4.9%
I 34823
 
4.4%
R 29861
 
3.7%
V 24813
 
3.1%
Other values (18) 130480
16.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 795685
99.9%
Dash Punctuation 958
 
0.1%
Space Separator 64
 
< 0.1%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 107438
13.5%
A 105606
13.3%
S 103700
13.0%
T 89900
11.3%
L 88742
11.2%
O 42598
 
5.4%
N 38750
 
4.9%
I 34823
 
4.4%
R 29861
 
3.8%
V 24813
 
3.1%
Other values (15) 129454
16.3%
Dash Punctuation
ValueCountFrequency (%)
- 958
100.0%
Space Separator
ValueCountFrequency (%)
64
100.0%
Other Punctuation
ValueCountFrequency (%)
! 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 795685
99.9%
Common 1026
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 107438
13.5%
A 105606
13.3%
S 103700
13.0%
T 89900
11.3%
L 88742
11.2%
O 42598
 
5.4%
N 38750
 
4.9%
I 34823
 
4.4%
R 29861
 
3.8%
V 24813
 
3.1%
Other values (15) 129454
16.3%
Common
ValueCountFrequency (%)
- 958
93.4%
64
 
6.2%
! 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 796711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 107438
13.5%
A 105606
13.3%
S 103700
13.0%
T 89900
11.3%
L 88742
11.1%
O 42598
 
5.3%
N 38750
 
4.9%
I 34823
 
4.4%
R 29861
 
3.7%
V 24813
 
3.1%
Other values (18) 130480
16.4%

Model
Text

Distinct127
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:19.727958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.3616117
Min length2

Characters and Unicode

Total characters913502
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowX5
2nd rowESCAPE
3rd rowBOLT EV
4th rowMODEL S
5th rowC-MAX
ValueCountFrequency (%)
model 65504
28.7%
3 26766
11.7%
y 26194
 
11.5%
leaf 13093
 
5.7%
s 7542
 
3.3%
bolt 6683
 
2.9%
ev 5826
 
2.6%
x 5002
 
2.2%
volt 4884
 
2.1%
prime 3957
 
1.7%
Other values (124) 62615
27.5%
2023-10-11T23:48:20.084058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 108730
11.9%
L 99317
 
10.9%
O 92792
 
10.2%
84470
 
9.2%
M 76372
 
8.4%
D 70977
 
7.8%
A 39294
 
4.3%
3 30770
 
3.4%
I 30443
 
3.3%
Y 28357
 
3.1%
Other values (28) 251980
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 759298
83.1%
Space Separator 84470
 
9.2%
Decimal Number 57653
 
6.3%
Dash Punctuation 9242
 
1.0%
Other Punctuation 2839
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 108730
14.3%
L 99317
13.1%
O 92792
12.2%
M 76372
10.1%
D 70977
9.3%
A 39294
 
5.2%
I 30443
 
4.0%
Y 28357
 
3.7%
R 28340
 
3.7%
T 23221
 
3.1%
Other values (15) 161455
21.3%
Decimal Number
ValueCountFrequency (%)
3 30770
53.4%
0 6775
 
11.8%
4 6536
 
11.3%
5 6131
 
10.6%
6 2825
 
4.9%
1 2604
 
4.5%
9 1317
 
2.3%
2 489
 
0.8%
8 149
 
0.3%
7 57
 
0.1%
Space Separator
ValueCountFrequency (%)
84470
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9242
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2839
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 759298
83.1%
Common 154204
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 108730
14.3%
L 99317
13.1%
O 92792
12.2%
M 76372
10.1%
D 70977
9.3%
A 39294
 
5.2%
I 30443
 
4.0%
Y 28357
 
3.7%
R 28340
 
3.7%
T 23221
 
3.1%
Other values (15) 161455
21.3%
Common
ValueCountFrequency (%)
84470
54.8%
3 30770
 
20.0%
- 9242
 
6.0%
0 6775
 
4.4%
4 6536
 
4.2%
5 6131
 
4.0%
. 2839
 
1.8%
6 2825
 
1.8%
1 2604
 
1.7%
9 1317
 
0.9%
Other values (3) 695
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 913502
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 108730
11.9%
L 99317
 
10.9%
O 92792
 
10.2%
84470
 
9.2%
M 76372
 
8.4%
D 70977
 
7.8%
A 39294
 
4.3%
3 30770
 
3.4%
I 30443
 
3.3%
Y 28357
 
3.1%
Other values (28) 251980
27.6%

Electric Vehicle Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Battery Electric Vehicle (BEV)
110865 
Plug-in Hybrid Electric Vehicle (PHEV)
32731 

Length

Max length38
Median length30
Mean length31.823505
Min length30

Characters and Unicode

Total characters4569728
Distinct characters23
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlug-in Hybrid Electric Vehicle (PHEV)
2nd rowPlug-in Hybrid Electric Vehicle (PHEV)
3rd rowBattery Electric Vehicle (BEV)
4th rowBattery Electric Vehicle (BEV)
5th rowPlug-in Hybrid Electric Vehicle (PHEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 110865
77.2%
Plug-in Hybrid Electric Vehicle (PHEV) 32731
 
22.8%

Length

2023-10-11T23:48:20.194334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T23:48:20.281559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
electric 143596
23.7%
vehicle 143596
23.7%
battery 110865
18.3%
bev 110865
18.3%
plug-in 32731
 
5.4%
hybrid 32731
 
5.4%
phev 32731
 
5.4%

Most occurring characters

ValueCountFrequency (%)
e 541653
11.9%
463519
10.1%
c 430788
9.4%
t 365326
 
8.0%
i 352654
 
7.7%
l 319923
 
7.0%
V 287192
 
6.3%
r 287192
 
6.3%
E 287192
 
6.3%
B 221730
 
4.9%
Other values (13) 1012559
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2859248
62.6%
Uppercase Letter 927038
 
20.3%
Space Separator 463519
 
10.1%
Open Punctuation 143596
 
3.1%
Close Punctuation 143596
 
3.1%
Dash Punctuation 32731
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 541653
18.9%
c 430788
15.1%
t 365326
12.8%
i 352654
12.3%
l 319923
11.2%
r 287192
10.0%
y 143596
 
5.0%
h 143596
 
5.0%
a 110865
 
3.9%
u 32731
 
1.1%
Other values (4) 130924
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
V 287192
31.0%
E 287192
31.0%
B 221730
23.9%
P 65462
 
7.1%
H 65462
 
7.1%
Space Separator
ValueCountFrequency (%)
463519
100.0%
Open Punctuation
ValueCountFrequency (%)
( 143596
100.0%
Close Punctuation
ValueCountFrequency (%)
) 143596
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32731
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3786286
82.9%
Common 783442
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 541653
14.3%
c 430788
11.4%
t 365326
9.6%
i 352654
9.3%
l 319923
8.4%
V 287192
7.6%
r 287192
7.6%
E 287192
7.6%
B 221730
5.9%
y 143596
 
3.8%
Other values (9) 549040
14.5%
Common
ValueCountFrequency (%)
463519
59.2%
( 143596
 
18.3%
) 143596
 
18.3%
- 32731
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4569728
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 541653
11.9%
463519
10.1%
c 430788
9.4%
t 365326
 
8.0%
i 352654
 
7.7%
l 319923
 
7.0%
V 287192
 
6.3%
r 287192
 
6.3%
E 287192
 
6.3%
B 221730
 
4.9%
Other values (13) 1012559
22.2%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Eligibility unknown as battery range has not been researched
63954 
Clean Alternative Fuel Vehicle Eligible
62149 
Not eligible due to low battery range
17493 

Length

Max length60
Median length39
Mean length48.109223
Min length37

Characters and Unicode

Total characters6908292
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowClean Alternative Fuel Vehicle Eligible
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowClean Alternative Fuel Vehicle Eligible
5th rowNot eligible due to low battery range

Common Values

ValueCountFrequency (%)
Eligibility unknown as battery range has not been researched 63954
44.5%
Clean Alternative Fuel Vehicle Eligible 62149
43.3%
Not eligible due to low battery range 17493
 
12.2%

Length

2023-10-11T23:48:20.353444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-11T23:48:20.432820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
battery 81447
 
8.1%
range 81447
 
8.1%
not 81447
 
8.1%
eligible 79642
 
7.9%
eligibility 63954
 
6.3%
been 63954
 
6.3%
unknown 63954
 
6.3%
researched 63954
 
6.3%
has 63954
 
6.3%
as 63954
 
6.3%
Other values (7) 301075
29.8%

Most occurring characters

ValueCountFrequency (%)
e 970186
14.0%
865186
12.5%
l 553281
 
8.0%
i 539398
 
7.8%
n 525515
 
7.6%
a 479054
 
6.9%
t 450086
 
6.5%
r 352951
 
5.1%
b 288997
 
4.2%
g 225043
 
3.3%
Other values (16) 1658595
24.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5650914
81.8%
Space Separator 865186
 
12.5%
Uppercase Letter 392192
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 970186
17.2%
l 553281
9.8%
i 539398
9.5%
n 525515
9.3%
a 479054
8.5%
t 450086
8.0%
r 352951
 
6.2%
b 288997
 
5.1%
g 225043
 
4.0%
s 191862
 
3.4%
Other values (9) 1074541
19.0%
Uppercase Letter
ValueCountFrequency (%)
E 126103
32.2%
C 62149
15.8%
A 62149
15.8%
F 62149
15.8%
V 62149
15.8%
N 17493
 
4.5%
Space Separator
ValueCountFrequency (%)
865186
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6043106
87.5%
Common 865186
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 970186
16.1%
l 553281
 
9.2%
i 539398
 
8.9%
n 525515
 
8.7%
a 479054
 
7.9%
t 450086
 
7.4%
r 352951
 
5.8%
b 288997
 
4.8%
g 225043
 
3.7%
s 191862
 
3.2%
Other values (15) 1466733
24.3%
Common
ValueCountFrequency (%)
865186
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6908292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 970186
14.0%
865186
12.5%
l 553281
 
8.0%
i 539398
 
7.8%
n 525515
 
7.6%
a 479054
 
6.9%
t 450086
 
6.5%
r 352951
 
5.1%
b 288997
 
4.2%
g 225043
 
3.3%
Other values (16) 1658595
24.0%

Electric Range
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.495738
Minimum0
Maximum337
Zeros63954
Zeros (%)44.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:20.519013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19
Q3111
95-th percentile266
Maximum337
Range337
Interquartile range (IQR)111

Descriptive statistics

Standard deviation97.128735
Coefficient of variation (CV)1.3777958
Kurtosis-0.17827507
Mean70.495738
Median Absolute Deviation (MAD)19
Skewness1.1599663
Sum10122906
Variance9433.9912
MonotonicityNot monotonic
2023-10-11T23:48:20.607120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63954
44.5%
215 6472
 
4.5%
220 4154
 
2.9%
84 4039
 
2.8%
238 3550
 
2.5%
25 3494
 
2.4%
21 2617
 
1.8%
32 2551
 
1.8%
208 2538
 
1.8%
19 2535
 
1.8%
Other values (92) 47692
33.2%
ValueCountFrequency (%)
0 63954
44.5%
6 934
 
0.7%
8 37
 
< 0.1%
9 20
 
< 0.1%
10 163
 
0.1%
11 1
 
< 0.1%
12 163
 
0.1%
13 361
 
0.3%
14 1123
 
0.8%
15 86
 
0.1%
ValueCountFrequency (%)
337 74
 
0.1%
330 317
 
0.2%
322 1715
1.2%
308 504
 
0.4%
293 435
 
0.3%
291 2261
1.6%
289 636
 
0.4%
270 272
 
0.2%
266 1464
1.0%
265 131
 
0.1%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1373.3865
Minimum0
Maximum845000
Zeros140151
Zeros (%)97.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:20.696692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9450.0677
Coefficient of variation (CV)6.8808508
Kurtosis495.47826
Mean1373.3865
Median Absolute Deviation (MAD)0
Skewness11.679142
Sum1.9721281 × 108
Variance89303780
MonotonicityNot monotonic
2023-10-11T23:48:20.771805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 140151
97.6%
69900 1446
 
1.0%
31950 401
 
0.3%
52900 217
 
0.2%
32250 149
 
0.1%
54950 132
 
0.1%
59900 131
 
0.1%
39995 115
 
0.1%
36900 100
 
0.1%
44100 97
 
0.1%
Other values (21) 657
 
0.5%
ValueCountFrequency (%)
0 140151
97.6%
31950 401
 
0.3%
32250 149
 
0.1%
32995 3
 
< 0.1%
33950 75
 
0.1%
34995 64
 
< 0.1%
36800 53
 
< 0.1%
36900 100
 
0.1%
39995 115
 
0.1%
43700 11
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 12
< 0.1%
110950 22
< 0.1%
109000 7
 
< 0.1%
102000 16
< 0.1%
98950 19
< 0.1%
91250 5
 
< 0.1%
90700 18
< 0.1%
89100 6
 
< 0.1%
81100 18
< 0.1%

Legislative District
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)< 0.1%
Missing327
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean29.371748
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:20.854617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118
median33
Q343
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.823899
Coefficient of variation (CV)0.50469923
Kurtosis-1.0638477
Mean29.371748
Median Absolute Deviation (MAD)11
Skewness-0.48737339
Sum4208061
Variance219.74797
MonotonicityNot monotonic
2023-10-11T23:48:20.945218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 9553
 
6.7%
45 8807
 
6.1%
48 8045
 
5.6%
36 6270
 
4.4%
1 6209
 
4.3%
5 6044
 
4.2%
46 5727
 
4.0%
43 5553
 
3.9%
11 5423
 
3.8%
37 4318
 
3.0%
Other values (39) 77320
53.8%
ValueCountFrequency (%)
1 6209
4.3%
2 1540
 
1.1%
3 700
 
0.5%
4 1130
 
0.8%
5 6044
4.2%
6 1289
 
0.9%
7 676
 
0.5%
8 1485
 
1.0%
9 779
 
0.5%
10 2544
1.8%
ValueCountFrequency (%)
49 1954
 
1.4%
48 8045
5.6%
47 2503
 
1.7%
46 5727
4.0%
45 8807
6.1%
44 3582
 
2.5%
43 5553
3.9%
42 1991
 
1.4%
41 9553
6.7%
40 3242
 
2.3%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct143596
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0920154 × 108
Minimum4385
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:21.032618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile1.0483427 × 108
Q11.6683379 × 108
median2.1110387 × 108
Q32.3645197 × 108
95-th percentile3.495658 × 108
Maximum4.7925477 × 108
Range4.7925039 × 108
Interquartile range (IQR)69618176

Descriptive statistics

Standard deviation83536997
Coefficient of variation (CV)0.39931348
Kurtosis3.2174821
Mean2.0920154 × 108
Median Absolute Deviation (MAD)30391378
Skewness0.96041658
Sum3.0040505 × 1013
Variance6.9784298 × 1015
MonotonicityNot monotonic
2023-10-11T23:48:21.125018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
218985539 1
 
< 0.1%
109692810 1
 
< 0.1%
103335277 1
 
< 0.1%
221103081 1
 
< 0.1%
185394884 1
 
< 0.1%
228669031 1
 
< 0.1%
229946503 1
 
< 0.1%
116891064 1
 
< 0.1%
185924105 1
 
< 0.1%
232654313 1
 
< 0.1%
Other values (143586) 143586
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
23145 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
Distinct790
Distinct (%)0.6%
Missing25
Missing (%)< 0.1%
Memory size1.1 MiB
2023-10-11T23:48:21.465874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length46
Median length45
Mean length38.518002
Min length24

Characters and Unicode

Total characters5530068
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique260 ?
Unique (%)0.2%

Sample

1st rowPOINT (-122.38242499999996 47.77279000000004)
2nd rowPOINT (-120.37951169999997 46.55609000000004)
3rd rowPOINT (-122.37275999999997 47.689685000000054)
4th rowPOINT (-122.15733999999998 47.487175000000036)
5th rowPOINT (-122.65223 47.57192)
ValueCountFrequency (%)
point 143571
33.3%
47.67668 3689
 
0.9%
122.12302 3689
 
0.9%
122.1873 2605
 
0.6%
47.82024500000006 2605
 
0.6%
122.20263999999997 2526
 
0.6%
47.67850000000004 2526
 
0.6%
122.201905 2372
 
0.6%
47.61385 2372
 
0.6%
122.16936999999996 2359
 
0.5%
Other values (1571) 262399
60.9%
2023-10-11T23:48:22.053332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 784420
14.2%
9 741529
13.4%
2 419696
 
7.6%
4 346681
 
6.3%
1 321441
 
5.8%
7 299618
 
5.4%
. 287142
 
5.2%
287142
 
5.2%
5 273302
 
4.9%
6 212135
 
3.8%
Other values (10) 1556962
28.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3807216
68.8%
Uppercase Letter 717855
 
13.0%
Other Punctuation 287142
 
5.2%
Space Separator 287142
 
5.2%
Dash Punctuation 143571
 
2.6%
Open Punctuation 143571
 
2.6%
Close Punctuation 143571
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 784420
20.6%
9 741529
19.5%
2 419696
11.0%
4 346681
9.1%
1 321441
8.4%
7 299618
 
7.9%
5 273302
 
7.2%
6 212135
 
5.6%
3 204783
 
5.4%
8 203611
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
O 143571
20.0%
T 143571
20.0%
N 143571
20.0%
I 143571
20.0%
P 143571
20.0%
Other Punctuation
ValueCountFrequency (%)
. 287142
100.0%
Space Separator
ValueCountFrequency (%)
287142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 143571
100.0%
Open Punctuation
ValueCountFrequency (%)
( 143571
100.0%
Close Punctuation
ValueCountFrequency (%)
) 143571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4812213
87.0%
Latin 717855
 
13.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 784420
16.3%
9 741529
15.4%
2 419696
8.7%
4 346681
7.2%
1 321441
 
6.7%
7 299618
 
6.2%
. 287142
 
6.0%
287142
 
6.0%
5 273302
 
5.7%
6 212135
 
4.4%
Other values (5) 839107
17.4%
Latin
ValueCountFrequency (%)
O 143571
20.0%
T 143571
20.0%
N 143571
20.0%
I 143571
20.0%
P 143571
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5530068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 784420
14.2%
9 741529
13.4%
2 419696
 
7.6%
4 346681
 
6.3%
1 321441
 
5.8%
7 299618
 
5.4%
. 287142
 
5.2%
287142
 
5.2%
5 273302
 
4.9%
6 212135
 
3.8%
Other values (10) 1556962
28.2%
Distinct76
Distinct (%)0.1%
Missing22
Missing (%)< 0.1%
Memory size1.1 MiB
2023-10-11T23:48:22.233569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.41355
Min length10

Characters and Unicode

Total characters6376631
Distinct characters36
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
2nd rowPACIFICORP
3rd rowCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
4th rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
5th rowPUGET SOUND ENERGY INC
ValueCountFrequency (%)
of 136292
12.6%
127530
11.8%
wa 88753
 
8.2%
tacoma 87639
 
8.1%
sound 85430
 
7.9%
energy 85430
 
7.9%
puget 84601
 
7.9%
inc||city 52866
 
4.9%
power 31402
 
2.9%
inc 28623
 
2.7%
Other values (114) 268938
25.0%
2023-10-11T23:48:22.495083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
933930
14.6%
O 469216
 
7.4%
N 451498
 
7.1%
T 444395
 
7.0%
A 431873
 
6.8%
E 415706
 
6.5%
I 348280
 
5.5%
C 347956
 
5.5%
Y 233259
 
3.7%
U 222191
 
3.5%
Other values (26) 2078327
32.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4826606
75.7%
Space Separator 933930
 
14.6%
Math Symbol 219085
 
3.4%
Close Punctuation 123719
 
1.9%
Dash Punctuation 123719
 
1.9%
Open Punctuation 123719
 
1.9%
Decimal Number 21450
 
0.3%
Other Punctuation 4403
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 469216
 
9.7%
N 451498
 
9.4%
T 444395
 
9.2%
A 431873
 
8.9%
E 415706
 
8.6%
I 348280
 
7.2%
C 347956
 
7.2%
Y 233259
 
4.8%
U 222191
 
4.6%
G 184355
 
3.8%
Other values (14) 1277877
26.5%
Decimal Number
ValueCountFrequency (%)
1 19952
93.0%
2 605
 
2.8%
3 602
 
2.8%
5 291
 
1.4%
Other Punctuation
ValueCountFrequency (%)
& 3811
86.6%
, 301
 
6.8%
# 291
 
6.6%
Space Separator
ValueCountFrequency (%)
933930
100.0%
Math Symbol
ValueCountFrequency (%)
| 219085
100.0%
Close Punctuation
ValueCountFrequency (%)
) 123719
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 123719
100.0%
Open Punctuation
ValueCountFrequency (%)
( 123719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4826606
75.7%
Common 1550025
 
24.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 469216
 
9.7%
N 451498
 
9.4%
T 444395
 
9.2%
A 431873
 
8.9%
E 415706
 
8.6%
I 348280
 
7.2%
C 347956
 
7.2%
Y 233259
 
4.8%
U 222191
 
4.6%
G 184355
 
3.8%
Other values (14) 1277877
26.5%
Common
ValueCountFrequency (%)
933930
60.3%
| 219085
 
14.1%
) 123719
 
8.0%
- 123719
 
8.0%
( 123719
 
8.0%
1 19952
 
1.3%
& 3811
 
0.2%
2 605
 
< 0.1%
3 602
 
< 0.1%
, 301
 
< 0.1%
Other values (2) 582
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6376631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
933930
14.6%
O 469216
 
7.4%
N 451498
 
7.1%
T 444395
 
7.0%
A 431873
 
6.8%
E 415706
 
6.5%
I 348280
 
5.5%
C 347956
 
5.5%
Y 233259
 
3.7%
U 222191
 
3.5%
Other values (26) 2078327
32.6%

2020 Census Tract
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2046
Distinct (%)1.4%
Missing22
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.2975903 × 1010
Minimum1.0810419 × 109
Maximum5.6033 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2023-10-11T23:48:22.596200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0810419 × 109
5-th percentile5.3011041 × 1010
Q15.3033009 × 1010
median5.3033029 × 1010
Q35.3053073 × 1010
95-th percentile5.3067012 × 1010
Maximum5.6033 × 1010
Range5.4951958 × 1010
Interquartile range (IQR)20063109

Descriptive statistics

Standard deviation1.59404 × 109
Coefficient of variation (CV)0.030089908
Kurtosis739.67077
Mean5.2975903 × 1010
Median Absolute Deviation (MAD)27107
Skewness-26.786723
Sum7.6059623 × 1015
Variance2.5409637 × 1018
MonotonicityNot monotonic
2023-10-11T23:48:23.398417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330282 × 10101656
 
1.2%
5.30330285 × 1010820
 
0.6%
5.303303232 × 1010683
 
0.5%
5.30330093 × 1010534
 
0.4%
5.303303232 × 1010494
 
0.3%
5.30330245 × 1010487
 
0.3%
5.30330241 × 1010469
 
0.3%
5.303302501 × 1010464
 
0.3%
5.306105211 × 1010460
 
0.3%
5.303303222 × 1010460
 
0.3%
Other values (2036) 137047
95.4%
ValueCountFrequency (%)
1081041901 1
< 0.1%
4013061064 1
< 0.1%
4013115900 1
< 0.1%
4013318700 1
< 0.1%
4013318800 1
< 0.1%
4013610301 1
< 0.1%
4013610302 1
< 0.1%
4013610500 1
< 0.1%
4013812900 1
< 0.1%
4013817600 1
< 0.1%
ValueCountFrequency (%)
5.60330001 × 10101
 
< 0.1%
5.60210007 × 10101
 
< 0.1%
5.307794001 × 10102
 
< 0.1%
5.307794001 × 10103
 
< 0.1%
5.307794 × 10101
 
< 0.1%
5.307794 × 10106
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.30770034 × 101030
< 0.1%
5.30770032 × 101031
< 0.1%

Interactions

2023-10-11T23:47:40.090580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:38.370989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:56.569558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:05.752139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:15.782571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:26.294569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:29.984242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:46.818286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:42.535810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:58.738137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:08.708622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:18.617636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:26.858393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:32.179741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:51.537213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:43.887001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:58.823350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:08.795064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:18.698945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:27.027476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:32.262334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:56.074670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:45.204239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:58.912401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:08.884305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:18.793605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:27.205198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:32.349717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:48:00.141853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:46.530217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:59.022953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:08.997438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:18.904796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:27.390527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:32.460557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:48:01.959200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:47.378965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:59.198019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:09.178024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:19.080802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:27.652100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:32.640630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:48:05.956544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:48.711686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:46:59.316479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:09.301286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:19.196704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:27.872964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-11T23:47:32.760795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-11T23:48:23.482725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Postal CodeModel YearElectric RangeBase MSRPLegislative DistrictDOL Vehicle ID2020 Census TractStateMakeElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) Eligibility
Postal Code1.000-0.0670.041-0.003-0.315-0.0100.0510.9970.1140.1980.148
Model Year-0.0671.000-0.688-0.195-0.0080.2540.0030.0360.2270.2130.512
Electric Range0.041-0.6881.0000.1060.000-0.140-0.0140.0080.3920.5310.657
Base MSRP-0.003-0.1950.1061.0000.009-0.0220.0000.0450.2490.0230.024
Legislative District-0.315-0.0080.0000.0091.000-0.017-0.1001.0000.0660.1440.101
DOL Vehicle ID-0.0100.254-0.140-0.022-0.0171.0000.0060.0070.1090.0770.360
2020 Census Tract0.0510.003-0.0140.000-0.1000.0061.0000.9930.1310.2140.168
State0.9970.0360.0080.0451.0000.0070.9931.0000.0000.0170.011
Make0.1140.2270.3920.2490.0660.1090.1310.0001.0000.7780.591
Electric Vehicle Type0.1980.2130.5310.0230.1440.0770.2140.0170.7781.0000.738
Clean Alternative Fuel Vehicle (CAFV) Eligibility0.1480.5120.6570.0240.1010.3600.1680.0110.5910.7381.000

Missing values

2023-10-11T23:48:16.016678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-11T23:48:16.340960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-11T23:48:16.766870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
05UXTA6C03PKingSeattleWA981772023BMWX5Plug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible30036218985539POINT (-122.38242499999996 47.77279000000004)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)53033001600
11FMCU0EZXNYakimaMoxeeWA989362022FORDESCAPEPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible38015197264322POINT (-120.37951169999997 46.55609000000004)PACIFICORP53077001702
21G1FW6S03JKingSeattleWA981172018CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible238036168549727POINT (-122.37275999999997 47.689685000000054)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)53033003000
35YJSA1AC0DKingNewcastleWA980592013TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible2086990041244891062POINT (-122.15733999999998 47.487175000000036)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)53033025005
41FADP5CU8FKitsapBremertonWA983122015FORDC-MAXPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range19026134915000POINT (-122.65223 47.57192)PUGET SOUND ENERGY INC53035081100
5WB523CF03PYakimaSelahWA989422023BMWIXBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0015236290862POINT (-120.54187999999999 46.654175000000066)PACIFICORP53077003200
6YV4BR0CK2KKingBellevueWA980042019VOLVOXC90Plug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range17048125426248POINT (-122.201905 47.61385)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)53033023701
75YJ3E1EA5KThurstonOlympiaWA985062019TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible220022477699071POINT (-122.88747809999995 47.051957300000026)PUGET SOUND ENERGY INC53067010200
85YJ3E1EB6KYakimaYakimaWA989022019TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible220014232637037POINT (-120.52401199999997 46.59739390000004)PACIFICORP53077000700
91N4AZ0CP9GKingRedmondWA980522016NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible84045178151032POINT (-122.12302 47.67668)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)53033032323
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric Utility2020 Census Tract
143586YV4BR0DL4NSnohomishWoodwayWA980202022VOLVOXC60Plug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range18032195840613POINT (-122.37507 47.80807000000004)PUGET SOUND ENERGY INC53061050600
1435875YJYGDEFXLChelanLeavenworthWA988262020TESLAMODEL YBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible291012109513708POINT (-120.66191529999998 47.59700830000003)PUD NO 1 OF CHELAN COUNTY53007960203
1435881N4BZ1CP5KSnohomishLynnwoodWA980362019NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible150032476655763POINT (-122.3164188 47.819664)PUGET SOUND ENERGY INC53061051930
1435895YJYGDEE8LKingSeattleWA981032020TESLAMODEL YBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible291043211223393POINT (-122.34300999999999 47.659185000000036)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)53033005100
1435905YJ3E1EA2PPierceUniversity PlaceWA984672023TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0028240744787POINT (-122.5404512 47.2074166)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY53053072312
143591WA1AAAGE1NSnohomishBothellWA980212022AUDIE-TRONBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched001199194831POINT (-122.17945799999995 47.80258900000007)PUGET SOUND ENERGY INC53061051926
1435922C4RC1S76NKingClyde HillWA980042022CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible32048193853824POINT (-122.201905 47.61385)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)53033024100
1435931G1FZ6S02LKingSeattleWA981042020CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible259043205856339POINT (-122.329075 47.6018)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)53033008200
1435941G1RD6E40ESpokaneSpokaneWA992082014CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible3806346114492POINT (-117.40724999999998 47.71862500000003)BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY53063010604
1435957SAXCDE55NSnohomishBothellWA980212022TESLAMODEL XBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched001220266560POINT (-122.17945799999995 47.80258900000007)PUGET SOUND ENERGY INC53061051918